GPU Dedicated Server for MXNet and Deep Learning

Navigation

Plans & Prices of GPU Servers for MXNet

We offer cost-effective and optimized NVIDIA GPU servers for MXNet.
Basic MXNet GPU
Nvidia Tesla K40

For high-performance computing and large data workloads, such as deep learning and AI reasoning.

Starting at

$109.00

/month

  • 64 GB RAM
  • Eight-Core Xeon E5-2670
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU: Nvidia Tesla K40
  • Microarchitecture: Kepler
  • Max GPU: 2
  • CUDA Cores: 2880
  • GPU Memory: 12GB
  • Performance: 4.29 TFLOPS
Professional MXNet GPU
Nvidia Tesla K80

For high-performance computing and large data workloads, such as deep learning and AI reasoning.

Starting at

$159.00

/month

  • 128 GB RAM
  • Dual 10-Core E5-2660v2
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows 10
  • GPU: Nvidia Tesla K80
  • Microarchitecture: Kepler
  • Max GPU: 2
  • CUDA Cores: 4992
  • GPU Memory: 24GB
  • Performance: 8.73 TFLOPS
Advanced MXNet GPU
Nvidia RTX A4000

RTX A4000 delivers real-time ray tracing, AI accelerated computing, and high-performance graphics to desktops.

Starting at

$209.00

/month

  • 128 GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows 10
  • GPU: Nvidia RTX A4000
  • Microarchitecture: Ampere
  • Max GPU: 2
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • Performance: 19.2 TFLOPS
Advanced MXNet GPU
Nvidia RTX A5000

RTX A5000 achieves an excellent balance between function, performance, and reliability. Assist designers, engineers, and artists to realize their visions.

Starting at

$269.00

/month

  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows 10
  • GPU: Nvidia RTX A5000
  • Microarchitecture: Ampere
  • Max GPU: 2
  • CUDA Cores: 8192
  • GPU Memory: 24GB GDDR6
  • Performance: 27.8 TFLOPS
New Arrival
Enterprise MXNet GPU
Nvidia A40

Accelerate data science and computation-based workloads. A40 is very suitable for AI and deep learning projects.

Starting at

$369.00

/month

  • 256 GB RAM
  • Dual E5-2697v4
  • 240GB SSD + 2TB SSD + 2TB NVMe
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows 10
  • GPU: Nvidia A40
  • Microarchitecture: Ampere
  • Max GPU: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB
  • Performance: 37.4 TFLOPS
New Arrival
Enterprise MXNet GPU
Nvidia V100

V100 server is a cloud product that can accelerate for more than 600 HPC applications and various deep learning frameworks.

Starting at

$369.00

/month

  • 256 GB RAM
  • Dual E5-2697v4
  • 240GB SSD + 2TB SSD + 2TB NVMe
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows 10
  • GPU: Nvidia V100
  • Microarchitecture: Volta
  • Max GPU: 1
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB
  • Performance: 14 TFLOPS

6 Reasons to Choose Our GPU Servers for MXNet

6 Reasons to Choose our GPU Servers for MXNet

DBM enables powerful MXNet GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.

Intel Xeon CPU

Intel Xeon CPU

Intel Xeon has extraordinary processing power and speed, which is very suitable for running deep learning frameworks. So you can totally use our Intel-Xeon-powered GPU Servers for MXNet.

SSD-Based Drives

SSD-Based Drives

You can never go wrong with our own top-notch GPU dedicated servers, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and 128 GB of RAM per server.

Full Root/Admin Access

Full Root/Admin Access

With full root/admin access, you will be able to take full control of your GPU dedicated server very easily and quickly.

99.9% Uptime Guarantee

99.9% Uptime Guarantee

With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for MXNet and networks.

Dedicated IP

Dedicated IP

One of the premium features is the dedicated IP address. Even the cheapest GPU dedicated hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.

DDoS Protection

DDoS Protection

Resources among different users are fully isolated to ensure your data security. DBM protects against DDoS from the edge fast while ensuring legitimate traffic of hosted GPUs for MXNet is not compromised.

Advantages of Deep Learning with MXNet

Advantages of Deep Learning with MXNet

Here are some of the areas in which MXNet compares favorably to existing alternatives.

User-Friendly and Easy-to-Use

User-Friendly and Easy-to-Use

MXNet has the NumPy-like programming interface and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start deep learning projects.

Hybrid Front-End

Hybrid Front-End

Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.

Rich Ecosystem

Rich Ecosystem

Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects, such as TVM, TensorRT, OpenVINO.

Distributed Training

Distributed Training

Scales up to multi-GPUs-and-distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.

Efficiency and Flexibility

Efficiency and Flexibility

Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.

10+ Language Bindings

10+ Language Bindings

Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, Julia , etc.

Feature Comparison: MXNet vs Keras vs PyTorch vs TensorFlow

Everyone's situation and needs are different, so it boils down to which features matter the most for your AI projects.
Features MXNet Keras PyTorch TensorFlow
API Level High and low High Low High and low
Architecture Complex, less readable Simple, concise, readable Complex, less readable Not easy to use
Datasets Large datasets, high performance Smaller datasets Large datasets, high performance Large datasets, high performance
Debugging Hard to debug pure symbol codes Simple network, so debugging is not often needed Good debugging capabilities Difficult to conduct debugging
Trained Models Included Yes Yes Yes Yes
Popularity Fourth most popular Most popular Third most popular Second most popular
Speed Fastest on ResNet-50, high performance Slow, low performance Fastest on Faster-RCNN, high performance Fastest on VGG-16, high performance
Written In C++, Python Python Lua, LuaJIT, C, CUDA, and C++ C++, CUDA, Python

FAQs of GPUs for MXNet

A list of frequently asked questions about GPU servers for MXNet.

What is MXNet used for?

MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It's highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. To get started Deep learning with MXNet, you often need GPUs for MXNet.

Who uses MXNet?

MXNet is supported by public cloud providers including Amazon Web Services (AWS) and Microsoft Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS.

What is the relationship between MXNet and TensorFlow?

Both MXNet and TensorFlow use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a mixed approach, with a dynamic dependency scheduler to combine symbolic and imperative programming.

What are the best GPUs for MXNet deep learning?

Today, leading vendor NVIDIA offers the best GPUs for MXNet deep learning in 2022. The models are the RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, Tesla K80, and Tesla K40. We will offer more suitable GPUs for MXNet in 2023.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for MXNet.

How can I run a MXNet model on multiple GPUs?

To do the multiple MXNet GPUs training, you need to initialize a model on all GPUs, split the batches of data into separate splits where each is stored on a different GPU and run the model separately on every split. The synchronization of gradients and parameters between GPUs is done automatically by Apache MXNet.

Is MXNet faster than PyTorch?

In Apache MXNet, you don't need to flatten the 4-D input into 2-D when feeding the data into forward pass. MXNet has the fastest training speed on ResNet-50, and PyTorch is the fastest on Faster-RCNN. Though you need to be cautious with such toy comparisons.

Is MXNet better than TensorFlow?

Compared to TensorFlow, MXNet has a smaller open-source community. Improvements, bug fixes, and other features take longer due to a lack of major community support. Despite being widely used by many organizations in the tech industry, MXNet is not as popular as Tensorflow.

What is the advantage of bare metal GPUs for MXNet?

Bare metal GPU servers for MXNet will provide you with an improved application and data performance while maintaining high-level security. When there is no virtualization, there is no overhead for a hypervisor, so the performance benefits. Most virtual environments and cloud solutions come with security risks.
DBM GPU Servers for MXNet are all bare metal servers, so we have best GPU dedicated server for AI.

Get Started Resources

Get what you need to get started with MXNet quickly.

Prerequisites

  • 1. Choose a plan and place an order
  • 2. Ubuntu 16.04 or higher, Windows 10 or higher
  • 3. Install NVIDIA® CUDA® Toolkit & cuDNN
  • 4. Python 3.6 - 3.8 recommended

Step-by-Step Instructions

Go to MXNet's site, read the installation guide.

  • 1. Indicate your preferred configuration to see specific instructions.
    Sample:
    Instruction
  • 2. Install MXNet with pip
    Make sure your installed CUDA version matches the CUDA version in the pip package.
    • Sample:
    • pip install --upgrade pip
    • pip install mxnet-cu112
  • 3. Verify the installation
    • Sample:
    • # Python with GPU, use mx.gpu(), to set MXNet context to be GPUs
    • import mxnet as mx
    • a = mx.nd.ones((2, 3), mx.gpu())
    • b = a * 2 + 1
    • b.asnumpy()
    • # If you don't get an import error, then MXNet is ready for python.

Apache MXNet Tutorials

Contact Us and Get a 3-Day Trial Now!

Leave us a note when purchasing, or contact us to apply a trial GPU server. You have enough time to test the performance, network latency, compatibility, multiple instance capacity, etc.

Contact Us
Recommend Friends, Get Credits

$20 will be credited to your account once you recommend a new client to purchase servers. Rewards can be superimposed.

Join Affiliate Program
Hosted Nvidia GPU servers